Affiliation:
1. School of Automation and Engineering, South China University of Technology, Guangzhou 510641, China
2. China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou 511370, China
3. Key Laboratory of Science and Technology on Reliability Physics and Application of Electronic Component, Guangzhou 511370, China
Abstract
The problem of health status prediction of insulated-gate bipolar transistors (IGBTs) has gained significant attention in the field of health management of power electronic equipment. The performance degradation of the IGBT gate oxide layer is one of the most important failure modes. In view of failure mechanism analysis and the easy implementation of monitoring circuits, this paper selects the gate leakage current of an IGBT as the precursor parameter of gate oxide degradation, and uses time domain characteristic analysis, gray correlation degree, Mahalanobis distance, Kalman filter, and other methods to carry out feature selection and fusion. Finally, it obtains a health indicator, characterizing the degradation of IGBT gate oxide. The degradation prediction model of the IGBT gate oxide layer is constructed by the Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) Network, which show the highest fitting accuracy compared with Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and CNN-LSTM models in our experiment. The extraction of the health indicator and the construction and verification of the degradation prediction model are carried out on the dataset released by the NASA-Ames Laboratory, and the average absolute error of performance degradation prediction is as low as 0.0216. These results show the feasibility of the gate leakage current as a precursor parameter of IGBT gate oxide layer failure, as well as the accuracy and reliability of the CNN-LSTM prediction model.
Funder
Key-Area Research and Development Program of Guangdong Province
National Natural Science Foundation of China
Opening Project (No. 21D03) of Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering
Reference25 articles.
1. Kabir, A., Bailey, C., and Lu, H. (2012, January 9–13). A review of data-driven prognostics in power electronics. Proceedings of the IEEE 35th International Spring Seminar on Electronics Technology (ISSE), Bad Aussee, Austria.
2. Recent advances and trend of HEV/EV-oriented power semiconductors-an overview;Liu;IET Power Electron.,2020
3. A review of IGBT models;Sheng;IEEE Trans. Power Electron.,2000
4. A Literature Review of IGBT Fault Diagnostic and Protection Methods for Power Inverters;Lu;IEEE Trans. Ind. Appl.,2009
5. Physics-of-failure, condition monitoring, prognostics of insulated gate bipolar transistor modules: A review;Oh;IEEE Trans. Power Electron.,2015